On a strategy to develop robust and simple tariffs from motor vehicle insurance data

The goals of this paper are twofold: we describe common features in data sets from motor vehicle insurance companies and we investigate a general strategy which exploits the knowledge of such features. The results of the strategy are a basis to develop insurance tariffs. The strategy is applied to a data set from motor vehicle insurance companies. We use a nonparametric approach based on a combination of kernel logistic regression and ¡support vector regression.

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Year of publication:	2004
Authors:	Christmann, Andreas
Institutions:	Institut für Wirtschafts- und Sozialstatistik, Universität Dortmund
Subject:	Classification \| Data Mining \| Insurance tariffs \| Kernel logistic regression \| Machine learning \| Regression \| Robustness \| Simplicity \| Support Vector Machine \| Support Vector Regression

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Extent:	application/pdf
Series:	Technical Reports.
Type of publication:	Book / Working Paper
Language:	English
Notes:	Number 2004,16
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10009295203